In order to make effective use of a three-way catalyzer used to process CO, HC and NOx, which are toxic substances present in engine exhaust gases, the engine must be operated at the theoretical AFR.
In engines using a three-way catalyzer to process exhaust gases, an O.sub.2 sensor installed in the exhaust manifold is used to detect whether the combustion is on the rich or on the lean side, and the AFR is feedback-controlled to a theoretical AFR by adjusting the fuel supplied by a fuel injection valve based on the detected value.
However it is difficult to ensure sufficient response capacity from this kind of feedback control.
Tokkai Sho 60-145443, 63-41635 and 63-38645 published by Japanese Patent Office therefore disclose methods of improving the response and control precision by learning during a sampling period under a variety of different conditions, and applying correction values based on these learned values to control the AFR.
This system is applied to fuel injection devices of the L-Jetronic type, wherein the injection pulse width TI corresponding to the quantity of fuel required in one ignition cycle is given by the following relation: EQU TI=Tp.times.Co.times..alpha..times..alpha.m+Ts
where
Tp is the basic pulse width of a fuel injection. EQU Tp=K.times.Qa/N PA1 K is a constant, Qa is intake volume and N is engine speed PA1 .alpha. is an AFR feedback control coefficient calculated according to the deviation between the real mixing ratio and a predetermined target ratio. The mixing ratios are calculated from the AFR by the equation: PA1 Co are various correction coefficients to improve specific running conditions of the engine. PA1 Ts is a non-effectual pulse width.
______________________________________ Real mixing ratio = real fuel-air ratio/ theoretical fuel-air ratio Target mixing ratio = target fuel-air ratio/ theoretical fuel-air ratio AFR = 1/fuel-air ratio ______________________________________
Here, .alpha.m is an AFR learning correction coefficient introduced for the purpose of improving the response of the AFR correction. These parameters may be represented by a learning area for storage of AFR coefficients .alpha.m. This learning area is divided into a plurality of small areas with Tp and N as coordinates, and .alpha.m is updated in each small area.
In one small area, for example, when a certain set of predetermined conditions are satisfied, (e.g. the AFR feedback signal is sampled a certain number of times during feedback control), updated learned values are calculated from an intermediate value L.sub.MD, of .alpha. computed from the AFR sensor output and a learned value which previously occupied this small area, and the result of this calculation is stored in the same area.
This type of learning control is effective in reducing exhaust emissions. However, under transient conditions such as acceleration, the running condition of the engine varies widely during a sampling period so that high learning precision cannot be attained, and AFR values tend to become widely scattered temporarily.